Python for Computer Vision, Unlocking Image Processing and Machine Learning with Python, Jackson M.

Python for Computer Vision, Unlocking Image Processing and Machine Learning with Python, Jackson M.
    
Фрагмент из книги.
I mage segmentation is a crucial process in computer vision and image analysis that involves dividing an image into distinct, meaningful regions or segments. This technique is fundamental for simplifying and analyzing complex visual data, allowing for the extraction of valuable information from images. By segmenting an image, one can isolate and identify objects, boundaries, and features, which is essential for various applications such as object recognition, medical imaging, autonomous driving, and scene understanding.




WATERSHED ALGORITHM FOR IMAGE SEGMENTATION.
The Watershed Algorithm is a powerful and versatile technique used for image segmentation, particularly effective in separating overlapping or touching objects within an image. Inspired by the concept of watersheds in geography, where water flows to different basins, this algorithm treats image intensity values as a topographic surface, with regions of low intensity representing valleys and high intensity representing peaks.

The core idea of the Watershed Algorithm involves flooding the topographic surface from the identified seed points or markers, analogous to water rising in the valleys of a landscape. As the flooding progresses, it expands to fill neighboring regions until it encounters boundaries or other seeded regions. This process effectively segments the image into distinct regions based on the topographical structure, delineating the boundaries between different segments.

CONTENT.
Chapter 1: Image Segmentation.
Introduction to Image Segmentation.
Thresholding Techniques: Global, Adaptive, and Otsu's Method.
Watershed Algorithm for Image Segmentation.
Semantic Segmentation with Deep Learning (UNet, Fully Convolutional Networks).
Instance Segmentation with Mask R-CNN.
Chapter 2: Working with Videos.
Reading and Writing Videos with OpenCV.
Real-Time Video Processing.
Motion Detection and Tracking.
Optical Flow Analysis.
Background Subtraction Techniques.
Chapter 3: Augmented Reality with Python.
Introduction to Augmented Reality.
Marker-based AR with OpenCV.
Building an AR Application with ArUco Markers.
Augmented Reality with 3D Objects using OpenGL and OpenCV.
Chapter 4: Facial Recognition and Biometrics.
Introduction to Facial Recognition Systems.
Face Detection and Alignment.
Face Recognition with OpenCV and dlib.
Implementing a Facial Recognition System with FaceNet.
Exploring Other Biometrics: Fingerprint and Iris Recognition.
Chapter 5: Gesture Recognition and Human-Computer Interaction.
Introduction to Gesture Recognition.
Hand Detection and Tracking.
Gesture Recognition with Machine Learning.
Building a Virtual Mouse with Hand Gestures.
Exploring Human Pose Estimation.
Chapter 6: Object Tracking.
Introduction to Object Tracking.
Tracking Algorithms: MeanShift, CAMShift, and KLT Tracker.
Object Tracking with OpenCV: Single Object vs. Multiple Objects.
Implementing Object Tracking with Deep Learning.
Real-Time Tracking Applications.
Chapter 7: 3D Vision and Point Clouds.
Introduction to 3D Vision.
Working with Depth Maps and Point Clouds.
Stereo Vision with OpenCV.
3D Reconstruction Techniques.
Introduction to LIDAR and 3D Object Detection.
Chapter 8: Practical Projects and Case Studies.
Building a Real-Time Face Recognition System.
Implementing an Automated License Plate Recognition (ALPR) System.
Developing a Real-Time Hand Gesture Recognition Application.
Creating a Smart Surveillance System with Object Detection.
Case Studies: Real-World Applications of Computer Vision in Industry.
Chapter 9: Future Trends in Computer Vision.
Emerging Technologies in Computer Vision.
AI and Computer Vision: The Next Frontier.
Ethical Considerations in Computer Vision Applications.
The Future of Computer Vision: Challenges and Opportunities.



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Теги: учебник по информатике :: информатика :: компьютеры :: Jackson